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Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

Dairy cattle lameness represents one of the common concerns in intensive and commercial dairy farms. Lameness is characterized by gait-related behavioral changes in cows and multiple approaches are being utilized to associate these changes with lameness conditions including data from accelerometers, and other precision technologies. The objective was to evaluate the use of machine learning algorithms for the identification of lameness conditions in dairy cattle. In this study, 310 multiparous Holstein dairy cows from a herd in Northern Colorado were affixed with a leg-based accelerometer (Icerobotics® Inc, Edinburg, Scotland) to obtain the lying time (min/d), daily steps count (n/d), and daily change (n/d). Subsequently, study cows were monitored for 4 months and cows submitted for claw trimming (CT) were differentiated as receiving corrective claw trimming (CCT) or as being diagnosed with a lameness disorder and consequent therapeutic claw trimming (TCT) by a certified hoof trimmer. Cows not submitted to CT were considered healthy controls. A median filter was applied to smoothen the data by reducing inherent variability. Three different machine learning (ML) models were defined to fit each algorithm which included the conventional features (containing daily lying, daily steps, and daily change derived from the accelerometer), slope features (containing features extracted from each variable in Conventional feature), or all features (3 simple features and 3 slope features). Random forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Time series (ROCKET) were used as ML predictive approaches. For the classification of cows requiring CCT and TCT, ROCKET classifier performed better with accuracy (> 90%), ROC-AUC (> 74%), and F1 score (> 0.61) as compared to other algorithms. Slope features derived in this study increased the efficiency of algorithms as the better-performing models included All features explored. However, further classification of diseases into infectious and non-infectious events was not effective because none of the algorithms presented satisfactory model accuracy parameters. For the classification of observed cow locomotion scores into severely lame and moderately lame conditions, the ROCKET classifier demonstrated satisfactory accuracy (> 0.85), ROC-AUC (> 0.68), and F1 scores (> 0.44). We conclude that ML models using accelerometer data are helpful in the identification of lameness in cows but need further research to increase the granularity and accuracy of classification. [ABSTRACT FROM AUTHOR]

In addition to its influence on lighting energy consumption, lighting systems also impact the cooling and heating energy consumption, which originates from the use of artificial lighting and/or blinds. In this paper, we focus on understanding participants' lighting choices and the influence of user's lighting choices on energy consumption. We conducted an experiment using an immersive virtual environment in a single occupancy office and collected 120 participants' choices, of which 60 participants were placed in a north facing office and the other 60 participants were placed in a south facing office. The participants were asked to select their desired room lighting setting at 9am, 1pm and 5pm. The results indicate that the choices were dependent on the room conditions. The time-of-day influenced blind use choices in both orientations, however, it was a significant factor influencing lighting choices in the south facing office. In addition, participants' choices resulted in less energy consumption than the baseline settings in EnergyPlus, since the maximum lighting option was rarely used. The influence of office orientation, and time-of-day also influenced the energy savings, with the north facing office saving more energy than the south facing office based on the participants' choices. [ABSTRACT FROM AUTHOR]

Post-earthquake operations, such as search and rescue and damage assessment, require efficient and effective surveying technologies to rapidly capture the "as-damaged" state of buildings. Recent research has shown early feasibility of methods for compilation of as-damaged building information models (BIM) from as-damaged point cloud data and "as-built" models. Yet research efforts to develop and rigorously test appropriate methods are seriously hampered by the obvious scarcity of access for researchers to earthquake-damaged buildings for surveying specimens and hence the lack of terrestrial laser scanning data of post-earthquake buildings. Full-scale or reduced-scale physical models of building components can be built and damaged using a shaking table or other structural laboratory equipment, and these can be scanned, all at reasonable cost. However, equivalent full-scale building samples are unavailable. The solution is to synthesize accurate and representative data sets. A computational approach for compiling such data sets, including BIM modeling of damaged buildings and synthetic scan generation, is proposed. The approach was validated experimentally through compilation of two full-scale models of buildings damaged in earthquakes in Turkey. [ABSTRACT FROM AUTHOR]